Abstract
In this chapter we consider anomaly detection based on distance (similarity) measures. Our approach is to explore various possible scenarios in which an anomaly may arise. To keep things simple, in most of the chapter we illustrate basic concepts using one-dimensional observations. Distance based algorithms, proposed by researchers, are presented in Chap. 6.
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Notes
- 1.
In SNN, the number of common points between k-nearest neighbors of two points represents the desired similarity between the points. This measure is described in more detail in Chap. 6.
References
S. Boriah, V. Chandola, V. Kumar, “Similarity measures for categorical data: A comparative evaluation.” Red 30(2), 3 (2008)
J. Han, M. Kamber, J. Pei, “Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems),” 2006
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Mehrotra, K.G., Mohan, C.K., Huang, H. (2017). Distance-Based Anomaly Detection Approaches. In: Anomaly Detection Principles and Algorithms. Terrorism, Security, and Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-67526-8_3
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DOI: https://doi.org/10.1007/978-3-319-67526-8_3
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